江汉大学学报（自然科学版）2024，Vol.52Issue(3)：74-86,13.DOI:10.16389/j.cnki.cn42-1737/n.2024.03.008
基于投票网络解决样本非均衡的入侵检测识别模型
Voting-based Framework for Auto Cyber Intrusion Detection System in Imbalanced Dataset Environment
摘要
Abstract
Modern cyber attack intrusion detection systems apply network flows with artificial labels to build the ability to detect potential threats automatically.Errors,sample insufficiency,and lack of essential features in artificial labeling would severely restrict the system's capability.It is a fatal flaw that the system could not discern attacking samples from benign samples.Most researchers regard the overall performance measurements as the benchmarks for intrusion detection systems while omitting what they are.It was created to warn people about dangerous network attacks.Hence,the article proposed a voting-based framework for an auto cyber intrusion detection system in an imbalanced dataset environment.Based on the trainable voting network,the framework integrated machine learning techniques and deep learning techniques to solve the problem of imbalanced datasets.The article focused on increasing the precision of fatal attack detection without compromising the system's overall performance.The experimental results suggest that the proposed model runs stable and well overall in these different datasets,and the model promotes the detection rate of the minority class effectively.关键词
入侵检测/网络攻击识别/不均衡样本数据集/深度学习/机器学习Key words
intrusion detection/cyber attack recognition/imbalanced sample dataset/deep learning/machine learning分类
信息技术与安全科学引用本文复制引用
李熙,梅倩,陶洁,余嘉伟,冯常奇..基于投票网络解决样本非均衡的入侵检测识别模型[J].江汉大学学报（自然科学版）,2024,52(3):74-86,13.基金项目
"新基建"视角下高职院校工科专业信息技术公共基础课程建设研究项目(2021-AFCEC-093) （2021-AFCEC-093）
